first commit

Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+test:
+	python -m pytest -vv --cov=app test_app.py
+
+format:
+	black *.py
+
+lint:
+	pylint --disable=R,C app.py
+
+all: install lint test format

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+# Load fine-tuned model and tokenizer from Hugging Face Hub
+model_name = "AICodexLab/answerdotai-ModernBERT-base-ai-detector"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Use pipeline for text classification
+classifier = pipeline(
+    "text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+
+# Define function for real-time AI text detection
+def predict_ai_text(input_text):
+    result = classifier(input_text)
+    label = "AI-Generated" if result[0]["label"] == "LABEL_1" else "Human-Written"
+    confidence = np.round(result[0]["score"], 3)
+    return f"{label} (Confidence: {confidence})"
+
+
+# Create Gradio interface
+app = gr.Interface(
+    fn=predict_ai_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."),
+    outputs=gr.Textbox(),
+    title="AI Text Detector",
+    description="Detect whether a given text is AI-generated or human-written.",
+    allow_flagging="never",
+)
+
+# Launch app
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+numpy
+pytest
+pytest-cov
+transformers
+torch
+gradio
+accelerate

test_app.py

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+import pytest
+from app import predict_ai_text
+
+
+def test_basic_ai_detection():
+    # Simple test input
+    text = "This article explores the evolution of artificial intelligence in modern society."
+
+    result = predict_ai_text(text)
+
+    # Check it's a string and includes expected keywords
+    assert isinstance(result, str)
+    assert "Confidence" in result
+    assert any(label in result for label in ["AI-Generated", "Human-Written"])
+
+
+def test_empty_input():
+    result = predict_ai_text("")
+    assert isinstance(result, str)
+    assert "Confidence" in result
+
+
+@pytest.mark.parametrize(
+    "text",
+    [
+        "The quick brown fox jumps over the lazy dog.",
+        "As technology advances, so does our understanding of machine learning.",
+        "Hello world!",
+    ],
+)
+def test_multiple_inputs(text):
+    result = predict_ai_text(text)
+    assert isinstance(result, str)
+    assert "Confidence" in result